def loadChunkData(iterator, feat, otherArgs):
# <feat> is KaldiArk object
global args
uttSpk, cmvnState, labelPdf, labelPho, toDo = otherArgs
# use CMVN
if args.useCMVN:
feat = E.use_cmvn(feat, cmvnState, uttSpk)
# Add delta
if args.delta > 0:
feat = E.add_delta(feat, args.delta)
# Splice front-back n frames
if args.splice > 0:
feat = feat.splice(args.splice)
# Transform to KaldiDict and sort them by frame length
feat = feat.array.sort(by='frame')
# Normalize
if args.normalizeChunk:
feat = feat.normalize()
# Concatenate label
datas = feat.concat([labelPdf, labelPho], axis=1)
# cut frames
if toDo == 'train':
if iterator.epoch >= 4:
datas = datas.cut(1000)
elif iterator.epoch >= 3:
datas = datas.cut(800)
elif iterator.epoch >= 2:
datas = datas.cut(400)
elif iterator.epoch >= 1:
datas = datas.cut(200)
elif iterator.epoch >= 0:
datas = datas.cut(100)
# Transform trainable numpy data
datas, _ = datas.merge(keepDim=True, sortFrame=True)
return datas
def loadChunkData(iterator, feat, otherArgs):
# <feat> is a KaldiArk object
global args
uttSpk, cmvnState, labelPdf, labelPho = otherArgs
# use CMVN
if args.useCMVN:
feat = E.use_cmvn(feat, cmvnState, uttSpk)
# Add delta
if args.delta > 0:
feat = E.add_delta(feat, args.delta)
# Splice front-back n frames
if args.splice > 0:
feat = feat.splice(args.splice)
# Transform to KaldiDict
feat = feat.array
# Normalize
if args.normalizeChunk:
feat = feat.normalize()
# Concatenate data-label in dimension
datas = feat.concat([labelPdf, labelPho], axis=1)
# Transform to trainable numpy data
datas, _ = datas.merge(keepDim=False, sort=False)
return datas
def decode_test(outDimPdf=1968, outDimPho=48):
global args
if args.preModel == '':
raise Exception("Expected Pretrained Model.")
elif not os.path.isfile(args.preModel):
raise Exception("No such file:{}.".format(args.preModel))
print("\n############## Parameters Configure ##############")
# Show configure information and write them to file
def configLog(message, f):
print(message)
f.write(message + '\n')
f = open(args.outDir + '/configure', "w")
configLog(
'Start System Time:{}'.format(
datetime.datetime.now().strftime("%Y-%m-%d %X")), f)
configLog('Host Name:{}'.format(socket.gethostname()), f)
configLog('Fix Random Seed:{}'.format(args.randomSeed), f)
configLog('GPU ID:{}'.format(args.gpu), f)
configLog('Pretrained Model:{}'.format(args.preModel), f)
configLog('Output Folder:{}'.format(args.outDir), f)
configLog('Use CMVN:{}'.format(args.useCMVN), f)
configLog('Splice N Frames:{}'.format(args.splice), f)
configLog('Add N Deltas:{}'.format(args.delta), f)
configLog('Normalize Chunk:{}'.format(args.normalizeChunk), f)
configLog('Normalize AMP:{}'.format(args.normalizeAMP), f)
configLog('Decode Minimum Active:{}'.format(args.minActive), f)
configLog('Decode Maximum Active:{}'.format(args.maxActive), f)
configLog('Decode Maximum Memory:{}'.format(args.maxMemory), f)
configLog('Decode Beam:{}'.format(args.beam), f)
configLog('Decode Lattice Beam:{}'.format(args.latBeam), f)
configLog('Decode Acoustic Weight:{}'.format(args.acwt), f)
configLog('Decode minimum Language Weight:{}'.format(args.minLmwt), f)
configLog('Decode maximum Language Weight:{}'.format(args.maxLmwt), f)
f.close()
print("\n############## Decode Test ##############")
#------------------ STEP 1: Load Pretrained Model ------------------
print('Load Model...')
# Initialize model
featDim = 40
if args.delta > 0:
featDim *= (args.delta + 1)
if args.splice > 0:
featDim *= (2 * args.splice + 1)
model = MLP(featDim, outDimPdf, outDimPho)
chainer.serializers.load_npz(args.preModel, model)
if args.gpu >= 0:
model.to_gpu(args.gpu)
#------------------ STEP 2: Prepare Test Data ------------------
print('Prepare decode test data...')
# Fmllr file
testFilePath = args.TIMITpath + '/data-fmllr-tri3/test/feats.scp'
testFeat = E.load(testFilePath)
# Use CMVN
if args.useCMVN:
testUttSpk = args.TIMITpath + '/data-fmllr-tri3/test/utt2spk'
testCmvnState = args.TIMITpath + '/data-fmllr-tri3/test/cmvn.ark'
testFeat = E.use_cmvn(testFeat, testCmvnState, testUttSpk)
# Add delta
if args.delta > 0:
testFeat = E.add_delta(testFeat, args.delta)
# Splice frames
if args.splice > 0:
testFeat = testFeat.splice(args.splice)
# Transform to array
testFeat = testFeat.array
# Normalize
if args.normalizeChunk:
testFeat = testFeat.normalize()
# Normalize acoustic model output
if args.normalizeAMP:
# Compute pdf counts in order to normalize acoustic model posterior probability.
countFile = args.outDir + '/pdfs_counts.txt'
# Get statistics file
if not os.path.isfile(countFile):
trainAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali/ali.*.gz'
_ = E.analyze_counts(aliFile=trainAliFile, outFile=countFile)
with open(countFile) as f:
line = f.readline().strip().strip("[]").strip()
# Get AMP bias value
counts = np.array(list(map(float, line.split())), dtype=np.float32)
normalizeBias = np.log(counts / np.sum(counts))
else:
normalizeBias = 0
#------------------ STEP 3: Decode ------------------
temp = E.KaldiDict()
print('Compute Test WER: Forward network', end=" " * 20 + '\r')
with chainer.using_config('train', False), chainer.no_backprop_mode():
for utt in testFeat.keys():
data = cp.array(testFeat[utt], dtype=cp.float32)
out1, out2 = model(data)
out = F.log_softmax(out1, axis=1)
out.to_cpu()
temp[utt] = out.array - normalizeBias
# Tansform KaldiDict to KaldiArk format
print('Compute Test WER: Transform to ark', end=" " * 20 + '\r')
amp = temp.ark
# Decode and obtain lattice
hmm = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_test/final.mdl'
hclg = args.TIMITpath + '/exp/tri3/graph/HCLG.fst'
lexicon = args.TIMITpath + '/exp/tri3/graph/words.txt'
print('Compute Test WER: Generate Lattice', end=" " * 20 + '\r')
lattice = E.decode_lattice(amp, hmm, hclg, lexicon, args.minActive,
args.maxActive, args.maxMemory, args.beam,
args.latBeam, args.acwt)
# Change language weight from 1 to 10, get the 1best words.
print('Compute Test WER: Get 1Best', end=" " * 20 + '\r')
outs = lattice.get_1best(lmwt=args.minLmwt,
maxLmwt=args.maxLmwt,
outFile=args.outDir + '/outRaw.txt')
#------------------ STEP 4: Score ------------------
# If reference file is not existed, make it.
phonemap = args.TIMITpath + '/conf/phones.60-48-39.map'
outFilter = args.TIMITpath + '/local/timit_norm_trans.pl -i - -m {} -from 48 -to 39'.format(
phonemap)
if not os.path.isfile(args.outDir + '/test_filt.txt'):
refText = args.TIMITpath + '/data/test/text'
cmd = 'cat {} | {} > {}/test_filt.txt'.format(refText, outFilter,
args.outDir)
(_, _) = E.run_shell_cmd(cmd)
# Score WER and find the smallest one.
print('Compute Test WER: compute WER', end=" " * 20 + '\r')
minWER = (None, None)
for k in range(args.minLmwt, args.maxLmwt + 1, 1):
cmd = 'cat {} | {} > {}/tanslation_{}.txt'.format(
outs[k], outFilter, args.outDir, k)
(_, _) = E.run_shell_cmd(cmd)
os.remove(outs[k])
score = E.wer('{}/test_filt.txt'.format(args.outDir),
"{}/tanslation_{}.txt".format(args.outDir, k),
mode='all')
if minWER[0] == None or score['WER'] < minWER[0]:
minWER = (score['WER'], k)
print("Best WER:{}％ at {}/tanslation_{}.txt".format(
minWER[0], args.outDir, k))
def train_model():
global args
print("\n############## Parameters Configure ##############")
# Show configure information and write them to file
def configLog(message, f):
print(message)
f.write(message + '\n')
f = open(args.outDir + '/configure', "w")
configLog(
'Start System Time:{}'.format(
datetime.datetime.now().strftime("%Y-%m-%d %X")), f)
configLog('Host Name:{}'.format(socket.gethostname()), f)
configLog('Fix Random Seed:{}'.format(args.randomSeed), f)
configLog('Mini Batch Size:{}'.format(args.batchSize), f)
configLog('GPU ID:{}'.format(args.gpu), f)
configLog('Train Epochs:{}'.format(args.epoch), f)
configLog('Output Folder:{}'.format(args.outDir), f)
configLog('Dropout Rate:{}'.format(args.dropout), f)
configLog('Use CMVN:{}'.format(args.useCMVN), f)
configLog('Splice N Frames:{}'.format(args.splice), f)
configLog('Add N Deltas:{}'.format(args.delta), f)
configLog('Normalize Chunk:{}'.format(args.normalizeChunk), f)
configLog('Normalize AMP:{}'.format(args.normalizeAMP), f)
configLog('Decode Minimum Active:{}'.format(args.minActive), f)
configLog('Decode Maximum Active:{}'.format(args.maxActive), f)
configLog('Decode Maximum Memory:{}'.format(args.maxMemory), f)
configLog('Decode Beam:{}'.format(args.beam), f)
configLog('Decode Lattice Beam:{}'.format(args.latBeam), f)
configLog('Decode Acoustic Weight:{}'.format(args.acwt), f)
configLog('Decode minimum Language Weight:{}'.format(args.minLmwt), f)
configLog('Decode maximum Language Weight:{}'.format(args.maxLmwt), f)
f.close()
print("\n############## Train DNN Acoustic Model ##############")
#------------------------ STEP 1: Prepare Training and Validation Data -----------------------------
print('Prepare Data Iterator...')
# Prepare fMLLR feature files
trainScpFile = args.TIMITpath + '/data-fmllr-tri3/train/feats.scp'
devScpFile = args.TIMITpath + '/data-fmllr-tri3/dev/feats.scp'
# Prepare training labels (alignment data)
trainAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali/ali.*.gz'
trainLabelPdf = E.get_ali(trainAliFile)
trainLabelPho = E.get_ali(trainAliFile, returnPhone=True)
for i in trainLabelPho.keys():
trainLabelPho[i] = trainLabelPho[i] - 1
# Prepare validation labels (alignment data)
devAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_dev/ali.*.gz'
devLabelPdf = E.get_ali(devAliFile)
devLabelPho = E.get_ali(devAliFile, returnPhone=True)
for i in devLabelPho.keys():
devLabelPho[i] = devLabelPho[i] - 1
# prepare CMVN files
trainUttSpk = args.TIMITpath + '/data-fmllr-tri3/train/utt2spk'
trainCmvnState = args.TIMITpath + '/data-fmllr-tri3/train/cmvn.ark'
devUttSpk = args.TIMITpath + '/data-fmllr-tri3/dev/utt2spk'
devCmvnState = args.TIMITpath + '/data-fmllr-tri3/dev/cmvn.ark'
# Now we try to make training-iterator and validation-iterator.
# Firstly, customize a function to process feature data.
def loadChunkData(iterator, feat, otherArgs):
# <feat> is a KaldiArk object
global args
uttSpk, cmvnState, labelPdf, labelPho = otherArgs
# use CMVN
if args.useCMVN:
feat = E.use_cmvn(feat, cmvnState, uttSpk)
# Add delta
if args.delta > 0:
feat = E.add_delta(feat, args.delta)
# Splice front-back n frames
if args.splice > 0:
feat = feat.splice(args.splice)
# Transform to KaldiDict
feat = feat.array
# Normalize
if args.normalizeChunk:
feat = feat.normalize()
# Concatenate data-label in dimension
datas = feat.concat([labelPdf, labelPho], axis=1)
# Transform to trainable numpy data
datas, _ = datas.merge(keepDim=False, sort=False)
return datas
# Then get data iterators
train = E.DataIterator(trainScpFile,
loadChunkData,
args.batchSize,
chunks=5,
shuffle=True,
otherArgs=(trainUttSpk, trainCmvnState,
trainLabelPdf, trainLabelPho))
print('Generate train dataset done. Chunks:{} / Batch size:{}'.format(
train.chunks, train.batchSize))
dev = E.DataIterator(devScpFile,
loadChunkData,
args.batchSize,
chunks=1,
shuffle=False,
otherArgs=(devUttSpk, devCmvnState, devLabelPdf,
devLabelPho))
print(
'Generate validation dataset done. Chunks:{} / Batch size:{}.'.format(
dev.chunks, dev.batchSize))
#--------------------------------- STEP 2: Prepare Model --------------------------
print('Prepare Model...')
# Initialize model
featDim = 40
if args.delta > 0:
featDim *= (args.delta + 1)
if args.splice > 0:
featDim *= (2 * args.splice + 1)
model = MLP(featDim, trainLabelPdf.target, trainLabelPho.target)
if args.gpu >= 0:
model.to_gpu(args.gpu)
# Initialize optimizer
lr = [(0, 0.08), (10, 0.04), (15, 0.02), (17, 0.01), (19, 0.005),
(22, 0.0025), (25, 0.001)]
print('Learning Rate (epoch,newLR):', lr)
optimizer = chainer.optimizers.MomentumSGD(lr[0][1], momentum=0.0)
lr.pop(0)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(0.0))
# Prepare a supporter to help handling training information.
supporter = E.Supporter(args.outDir)
#------------------ STEP 3: Prepare Decoding Test Data and Function ------------------
print('Prepare decoding test data...')
# fMLLR file of test data
testFilePath = args.TIMITpath + '/data-fmllr-tri3/test/feats.scp'
testFeat = E.load(testFilePath)
# Using CMVN
if args.useCMVN:
testUttSpk = args.TIMITpath + '/data-fmllr-tri3/test/utt2spk'
testCmvnState = args.TIMITpath + '/data-fmllr-tri3/test/cmvn.ark'
testFeat = E.use_cmvn(testFeat, testCmvnState, testUttSpk)
# Add delta
if args.delta > 0:
testFeat = E.add_delta(testFeat, args.delta)
# Splice frames
if args.splice > 0:
testFeat = testFeat.splice(args.splice)
# Transform to array
testFeat = testFeat.array
# Normalize
if args.normalizeChunk:
testFeat = testFeat.normalize()
# Normalize acoustic model output
if args.normalizeAMP:
# Compute pdf counts in order to normalize acoustic model posterior probability.
countFile = args.outDir + '/pdfs_counts.txt'
# Get statistics file
if not os.path.isfile(countFile):
_ = E.analyze_counts(aliFile=trainAliFile, outFile=countFile)
with open(countFile) as f:
line = f.readline().strip().strip("[]").strip()
# Get AMP bias value
counts = np.array(list(map(float, line.split())), dtype=np.float32)
normalizeBias = np.log(counts / np.sum(counts))
else:
normalizeBias = 0
# Now, design a function to compute WER score
def wer_fun(model, testFeat, normalizeBias):
global args
# Use decode test data to forward network
temp = E.KaldiDict()
print('(testing) Forward network', end=" " * 20 + '\r')
with chainer.using_config('train', False), chainer.no_backprop_mode():
for utt in testFeat.keys():
data = cp.array(testFeat[utt], dtype=cp.float32)
out1, out2 = model(data)
out = F.log_softmax(out1, axis=1)
out.to_cpu()
temp[utt] = out.array - normalizeBias
# Tansform KaldiDict to KaldiArk format
print('(testing) Transform to ark', end=" " * 20 + '\r')
amp = temp.ark
# Decoding to obtain a lattice
hmm = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_test/final.mdl'
hclg = args.TIMITpath + '/exp/tri3/graph/HCLG.fst'
lexicon = args.TIMITpath + '/exp/tri3/graph/words.txt'
print('(testing) Generate Lattice', end=" " * 20 + '\r')
lattice = E.decode_lattice(amp, hmm, hclg, lexicon, args.minActive,
args.maxActive, args.maxMemory, args.beam,
args.latBeam, args.acwt)
# Change language weight from 1 to 10, get the 1best words.
print('(testing) Get 1-best words', end=" " * 20 + '\r')
outs = lattice.get_1best(lmwt=args.minLmwt,
maxLmwt=args.maxLmwt,
outFile=args.outDir + '/outRaw.txt')
# If reference file is not existed, make it.
phonemap = args.TIMITpath + '/conf/phones.60-48-39.map'
outFilter = args.TIMITpath + '/local/timit_norm_trans.pl -i - -m {} -from 48 -to 39'.format(
phonemap)
if not os.path.isfile(args.outDir + '/test_filt.txt'):
refText = args.TIMITpath + '/data/test/text'
cmd = 'cat {} | {} > {}/test_filt.txt'.format(
refText, outFilter, args.outDir)
(_, _) = E.run_shell_cmd(cmd)
# Score WER and find the smallest one.
print('(testing) Score', end=" " * 20 + '\r')
minWER = None
for k in range(args.minLmwt, args.maxLmwt + 1, 1):
cmd = 'cat {} | {} > {}/test_prediction_filt.txt'.format(
outs[k], outFilter, args.outDir)
(_, _) = E.run_shell_cmd(cmd)
os.remove(outs[k])
score = E.wer('{}/test_filt.txt'.format(args.outDir),
"{}/test_prediction_filt.txt".format(args.outDir),
mode='all')
if minWER == None or score['WER'] < minWER:
minWER = score['WER']
return minWER
#-------------------------- STEP 4: Train Model ---------------------------
# While first epoch, the epoch size is computed gradually, so the prograss information will be inaccurate.
print('Now Start to Train')
print(
'Note that: The first epoch will be doing the statistics of total data size gradually.'
)
print(
'Note that: We will evaluate the WER of test dataset after epoch which will cost a few seconds.'
)
# Preprocessing batch data which is getten from data iterator
def convert(batch):
batch = cp.array(batch, dtype=cp.float32)
data = batch[:, 0:-2]
label1 = cp.array(batch[:, -2], dtype=cp.int32)
label2 = cp.array(batch[:, -1], dtype=cp.int32)
return data, label1, label2
# We will save model during training loop, so prepare a model-save function
def saveFunc(fileName, model):
global args
copymodel = model.copy()
if args.gpu >= 0:
copymodel.to_cpu()
chainer.serializers.save_npz(fileName, copymodel)
# Start training loop
for e in range(args.epoch):
supporter.send_report({'epoch': e})
print()
i = 1
usedTime = 0
# Train
while True:
start = time.time()
# Get data >> Forward network >> Loss back propagation >> Update
batch = train.next()
data, label1, label2 = convert(batch)
with chainer.using_config('Train', True):
h1, h2 = model(data)
L1 = F.softmax_cross_entropy(h1, label1)
L2 = F.softmax_cross_entropy(h2, label2)
loss = L1 + L2
acc = F.accuracy(F.softmax(h1, axis=1), label1)
model.cleargrads()
loss.backward()
optimizer.update()
# Compute time cost
ut = time.time() - start
usedTime += ut
print(
"(training) Epoch:{}>>>{}% Chunk:{}>>>{}% Iter:{} Used-time:{}s Batch-loss:{} Speed:{}iters/s"
.format(e, int(100 * train.epochProgress), train.chunk,
int(100 * train.chunkProgress), i, int(usedTime),
"%.4f" % (float(loss.array)), "%.2f" % (1 / ut)),
end=" " * 5 + '\r')
i += 1
supporter.send_report({'train_loss': loss, 'train_acc': acc})
# If forward all data, break
if train.isNewEpoch:
break
print()
i = 1
usedTime = 0
# Validate
while True:
start = time.time()
# Get data >> Forward network >> Score
batch = dev.next()
data, label1, label2 = convert(batch)
with chainer.using_config('train',
False), chainer.no_backprop_mode():
h1, h2 = model(data)
loss = F.softmax_cross_entropy(h1, label1)
acc = F.accuracy(F.softmax(h1, axis=1), label1)
# Compute time cost
ut = time.time() - start
usedTime += ut
print(
"(Validating) Epoch:{}>>>{}% Chunk:{}>>>{}% Iter:{} Used-time:{}s Batch-loss:{} Speed:{}iters/s"
.format(e, int(100 * dev.epochProgress), dev.chunk,
int(100 * dev.chunkProgress), i, int(usedTime),
"%.4f" % (float(loss.array)), "%.2f" % (1 / ut)),
end=" " * 5 + '\r')
i += 1
supporter.send_report({'dev_loss': loss, 'dev_acc': acc})
# If forward all data, break
if dev.isNewEpoch:
break
print()
# Compute WER score
WERscore = wer_fun(model, testFeat, normalizeBias)
supporter.send_report({'test_wer': WERscore, 'lr': optimizer.lr})
# Collect all information of this epoch that is reported before, and show them at display
supporter.collect_report(plot=True)
# Save model
supporter.save_model(saveFunc, models={'MLP': model})
# Change learning rate
if len(lr) > 0 and supporter.judge('epoch', '>=', lr[0][0]):
optimizer.lr = lr[0][1]
lr.pop(0)
print("DNN Acoustic Model training done.")
print("The final model has been saved as:", supporter.finalModel)
print('Over System Time:', datetime.datetime.now().strftime("%Y-%m-%d %X"))
def main():
# ------------- Parse arguments from command line ----------------------
# 1. Add a discription of this program
args.describe("This program is used to train monophone GMM-HMM model")
# 2. Add options
args.add("--expDir",
abbr="-e",
dtype=str,
default="exp",
discription="The data and output path of current experiment.")
args.add("--delta",
abbr="-d",
dtype=int,
default=2,
discription="Add n-order to feature.")
args.add("--numIters",
abbr="-n",
dtype=int,
default=40,
discription="How many iterations to train.")
args.add("--maxIterInc",
abbr="-m",
dtype=int,
default=30,
discription="The final iteration of increasing gaussians.")
args.add("--realignIter",
abbr="-r",
dtype=int,
default=[
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 23, 26, 29,
32, 35, 38
],
discription="the iteration to realign feature.")
args.add("--order",
abbr="-o",
dtype=int,
default=6,
minV=1,
maxV=6,
discription="Which N-grams model to use.")
args.add("--beam",
abbr="-b",
dtype=int,
default=13,
discription="Decode beam size.")
args.add("--latBeam",
abbr="-l",
dtype=int,
default=6,
discription="Lattice beam size.")
args.add("--acwt",
abbr="-a",
dtype=float,
default=0.083333,
discription="Acoustic model weight.")
args.add(
"--parallel",
abbr="-p",
dtype=int,
default=4,
minV=1,
maxV=10,
discription=
"The number of parallel process to compute feature of train dataset.")
args.add("--skipTrain",
abbr="-s",
dtype=bool,
default=False,
discription="If True, skip training. Do decoding only.")
# 3. Then start to parse arguments.
args.parse()
# 4. Take a backup of arguments
args.print_args() # print arguments to display
argsLogFile = os.path.join(args.expDir, "conf", "train_mono.args")
args.save(argsLogFile)
if not args.skipTrain:
# ------------- Prepare feature for training ----------------------
# 1. Load the feature for training (We use the index table format)
feat = exkaldi.load_index_table(
os.path.join(args.expDir, "mfcc", "train", "mfcc_cmvn.ark"))
print(f"Load MFCC+CMVN feature.")
feat = exkaldi.add_delta(feat,
order=args.delta,
outFile=os.path.join(args.expDir,
"train_mono",
"mfcc_cmvn_delta.ark"))
print(f"Add {args.delta}-order deltas.")
# 2. Load lexicon bank
lexicons = exkaldi.load_lex(
os.path.join(args.expDir, "dict", "lexicons.lex"))
print(f"Restorage lexicon bank.")
# ------------- Start training ----------------------
# 1. Initialize a monophone HMM object
model = exkaldi.hmm.MonophoneHMM(lexicons=lexicons, name="mono")
model.initialize(feat=feat,
topoFile=os.path.join(args.expDir, "dict", "topo"))
print(f"Initialized a monophone HMM-GMM model: {model.info}.")
# 2. Split data for parallel training
transcription = exkaldi.load_transcription(
os.path.join(args.expDir, "data", "train", "text"))
transcription = transcription.sort()
if args.parallel > 1:
# split feature
feat = feat.sort(by="utt").subset(chunks=args.parallel)
# split transcription depending on utterance IDs of each feature
temp = []
for f in feat:
temp.append(transcription.subset(keys=f.utts))
transcription = temp
# 3. Train
model.train(
feat,
transcription,
LFile=os.path.join(args.expDir, "dict", "L.fst"),
tempDir=os.path.join(args.expDir, "train_mono"),
numIters=args.numIters,
maxIterInc=args.maxIterInc,
totgauss=1000,
realignIter=args.realignIter,
boostSilence=1.0,
)
print(model.info)
# Save the tree
model.tree.save(os.path.join(args.expDir, "train_mono", "tree"))
print(f"Tree has been saved.")
# 4. Realign with boostSilence 1.25
print("Realign the training feature (boost silence = 1.25)")
trainGraphFiles = exkaldi.utils.list_files(
os.path.join(args.expDir, "train_mono", "*train_graph"))
model.align(
feat,
trainGraphFile=
trainGraphFiles, # train graphs have been generated in the train step.
boostSilence=1.25, #1.5
outFile=os.path.join(args.expDir, "train_mono", "final.ali"))
del feat
print("Save the new alignment done.")
tree = model.tree
else:
declare.is_file(os.path.join(args.expDir, "train_mono", "final.mdl"))
declare.is_file(os.path.join(args.expDir, "train_mono", "tree"))
model = exkaldi.load_hmm(
os.path.join(args.expDir, "train_mono", "final.mdl"))
tree = exkaldi.load_tree(
os.path.join(args.expDir, "train_mono", "tree"))
# ------------- Compile WFST training ----------------------
# Make a WFST decoding graph
make_WFST_graph(
outDir=os.path.join(args.expDir, "train_mono", "graph"),
hmm=model,
tree=tree,
)
# Decode test data
GMM_decode_mfcc_and_score(
outDir=os.path.join(args.expDir, "train_mono",
f"decode_{args.order}grams"),
hmm=model,
HCLGfile=os.path.join(args.expDir, "train_mono", "graph",
f"HCLG.{args.order}.fst"),
)
def main():
# ------------- Parse arguments from command line ----------------------
# 1. Add a discription of this program
args.discribe("This program is used to train triphone GMM-HMM model")
# 2. Add options
args.add("--expDir",
abbr="-e",
dtype=str,
default="exp",
discription="The data and output path of current experiment.")
args.add("--delta",
abbr="-d",
dtype=int,
default=2,
discription="Add n-order to feature.")
args.add("--numIters",
abbr="-n",
dtype=int,
default=35,
discription="How many iterations to train.")
args.add("--maxIterInc",
abbr="-m",
dtype=int,
default=25,
discription="The final iteration of increasing gaussians.")
args.add("--realignIter",
abbr="-r",
dtype=int,
default=[10, 20, 30],
discription="the iteration to realign feature.")
args.add("--order",
abbr="-o",
dtype=int,
default=6,
discription="Which N-grams model to use.")
args.add("--beam",
abbr="-b",
dtype=int,
default=13,
discription="Decode beam size.")
args.add("--latBeam",
abbr="-l",
dtype=int,
default=6,
discription="Lattice beam size.")
args.add("--acwt",
abbr="-a",
dtype=float,
default=0.083333,
discription="Acoustic model weight.")
args.add(
"--parallel",
abbr="-p",
dtype=int,
default=4,
minV=1,
maxV=10,
discription=
"The number of parallel process to compute feature of train dataset.")
args.add("--skipTrain",
abbr="-s",
dtype=bool,
default=False,
discription="If True, skip training. Do decoding only.")
# 3. Then start to parse arguments.
args.parse()
# 4. Take a backup of arguments
argsLogFile = os.path.join(args.expDir, "conf", "train_delta.args")
args.save(argsLogFile)
if not args.skipTrain:
# ------------- Prepare feature and previous alignment for training ----------------------
# 1. Load the feature for training
feat = exkaldi.load_index_table(
os.path.join(args.expDir, "mfcc", "train", "mfcc_cmvn.ark"))
print(f"Load MFCC+CMVN feature.")
feat = exkaldi.add_delta(feat,
order=args.delta,
outFile=os.path.join(args.expDir,
"train_delta",
"mfcc_cmvn_delta.ark"))
print(f"Add {args.delta}-order deltas.")
# 2. Load lexicon bank
lexicons = exkaldi.load_lex(
os.path.join(args.expDir, "dict", "lexicons.lex"))
print(f"Restorage lexicon bank.")
# 3. Load previous alignment
ali = exkaldi.load_index_table(os.path.join(args.expDir, "train_mono",
"*final.ali"),
useSuffix="ark")
# -------------- Build the decision tree ------------------------
print("Start build a tree")
tree = exkaldi.hmm.DecisionTree(lexicons=lexicons,
contextWidth=3,
centralPosition=1)
tree.train(
feat=feat,
hmm=os.path.join(args.expDir, "train_mono", "final.mdl"),
ali=ali,
topoFile=os.path.join(args.expDir, "dict", "topo"),
numLeaves=2500,
tempDir=os.path.join(args.expDir, "train_delta"),
)
print(f"Build tree done.")
# ------------- Start training ----------------------
# 1. Initialize a monophone HMM object
model = exkaldi.hmm.TriphoneHMM(lexicons=lexicons, name="mono")
model.initialize(
tree=tree,
topoFile=os.path.join(args.expDir, "dict", "topo"),
treeStatsFile=os.path.join(args.expDir, "train_delta",
"treeStats.acc"),
)
print(f"Initialized a monophone HMM-GMM model: {model.info}.")
# 2. convert the previous alignment
print(f"Transform the alignment")
newAli = exkaldi.hmm.convert_alignment(
ali=ali,
originHmm=os.path.join("exp", "train_mono", "final.mdl"),
targetHmm=model,
tree=tree,
outFile=os.path.join(args.expDir, "train_delta", "initial.ali"),
)
# 2. Split data for parallel training
transcription = exkaldi.load_transcription(
os.path.join(args.expDir, "data", "train", "text"))
transcription = transcription.sort()
if args.parallel > 1:
# split feature
feat = feat.sort(by="utt").subset(chunks=args.parallel)
# split transcription depending on utterance IDs of each feat
tempTrans = []
tempAli = []
for f in feat:
tempTrans.append(transcription.subset(keys=f.utts))
tempAli.append(newAli.subset(keys=f.utts))
transcription = tempTrans
newAli = tempAli
# 3. Train
print("Train the triphone model")
model.train(
feat,
transcription,
os.path.join("exp", "dict", "L.fst"),
tree,
tempDir=os.path.join(args.expDir, "train_delta"),
initialAli=newAli,
numIters=args.numIters,
maxIterInc=args.maxIterInc,
totgauss=15000,
realignIter=args.realignIter,
boostSilence=1.0,
)
print(model.info)
# Save the tree
model.tree.save(os.path.join(args.expDir, "train_delta", "tree"))
print(f"Tree has been saved.")
del feat
else:
declare.is_file(os.path.join(args.expDir, "train_delta", "final.mdl"))
declare.is_file(os.path.join(args.expDir, "train_delta", "tree"))
model = exkaldi.load_hmm(
os.path.join(args.expDir, "train_delta", "final.mdl"))
tree = exkaldi.load_tree(
os.path.join(args.expDir, "train_delta", "tree"))
# ------------- Compile WFST training ----------------------
# Make a WFST decoding graph
make_WFST_graph(
outDir=os.path.join(args.expDir, "train_delta", "graph"),
hmm=model,
tree=tree,
)
# Decode test data
GMM_decode_mfcc_and_score(
outDir=os.path.join(args.expDir, "train_delta",
f"decode_{args.order}grams"),
hmm=model,
HCLGfile=os.path.join(args.expDir, "train_delta", "graph",
f"HCLG.{args.order}.fst"),
)
def train_model():
global args
print("\n############## Parameters Configure ##############")
# Show configure information and write them to file
def configLog(message, f):
print(message)
f.write(message + '\n')
f = open(args.outDir + '/configure', "w")
configLog(
'Start System Time:{}'.format(
datetime.datetime.now().strftime("%Y-%m-%d %X")), f)
configLog('Host Name:{}'.format(socket.gethostname()), f)
configLog('Fix Random Seed:{}'.format(args.randomSeed), f)
configLog('Mini Batch Size:{}'.format(args.batchSize), f)
configLog('GPU ID:{}'.format(args.gpu), f)
configLog('Train Epochs:{}'.format(args.epoch), f)
configLog('Output Folder:{}'.format(args.outDir), f)
configLog('GRU layers:{}'.format(args.layer), f)
configLog('GRU hidden nodes:{}'.format(args.hiddenNode), f)
configLog('GRU dropout:{}'.format(args.dropout), f)
configLog('Use CMVN:{}'.format(args.useCMVN), f)
configLog('Splice N Frames:{}'.format(args.splice), f)
configLog('Add N Deltas:{}'.format(args.delta), f)
configLog('Normalize Chunk:{}'.format(args.normalizeChunk), f)
configLog('Normalize AMP:{}'.format(args.normalizeAMP), f)
configLog('Decode Minimum Active:{}'.format(args.minActive), f)
configLog('Decode Maximum Active:{}'.format(args.maxActive), f)
configLog('Decode Maximum Memory:{}'.format(args.maxMemory), f)
configLog('Decode Beam:{}'.format(args.beam), f)
configLog('Decode Lattice Beam:{}'.format(args.latBeam), f)
configLog('Decode Acoustic Weight:{}'.format(args.acwt), f)
configLog('Decode minimum Language Weight:{}'.format(args.minLmwt), f)
configLog('Decode maximum Language Weight:{}'.format(args.maxLmwt), f)
f.close()
print("\n############## Train GRU Acoustic Model ##############")
#----------------- STEP 1: Prepare Train Data -----------------
print('Prepare data iterator...')
# Fmllr data file
trainScpFile = args.TIMITpath + '/data-fmllr-tri3/train/feats.scp'
devScpFile = args.TIMITpath + '/data-fmllr-tri3/dev/feats.scp'
# Alignment label file
trainAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali/ali.*.gz'
devAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_dev/ali.*.gz'
# Load label
trainLabelPdf = E.load_ali(trainAliFile)
trainLabelPho = E.load_ali(trainAliFile, returnPhone=True)
for i in trainLabelPho.keys():
trainLabelPho[i] = trainLabelPho[i] - 1
devLabelPdf = E.load_ali(devAliFile)
devLabelPho = E.load_ali(devAliFile, returnPhone=True)
for i in devLabelPho.keys():
devLabelPho[i] = devLabelPho[i] - 1
# CMVN file
trainUttSpk = args.TIMITpath + '/data-fmllr-tri3/train/utt2spk'
trainCmvnState = args.TIMITpath + '/data-fmllr-tri3/train/cmvn.ark'
devUttSpk = args.TIMITpath + '/data-fmllr-tri3/dev/utt2spk'
devCmvnState = args.TIMITpath + '/data-fmllr-tri3/dev/cmvn.ark'
# Design a process function
def loadChunkData(iterator, feat, otherArgs):
# <feat> is KaldiArk object
global args
uttSpk, cmvnState, labelPdf, labelPho, toDo = otherArgs
# use CMVN
if args.useCMVN:
feat = E.use_cmvn(feat, cmvnState, uttSpk)
# Add delta
if args.delta > 0:
feat = E.add_delta(feat, args.delta)
# Splice front-back n frames
if args.splice > 0:
feat = feat.splice(args.splice)
# Transform to KaldiDict and sort them by frame length
feat = feat.array.sort(by='frame')
# Normalize
if args.normalizeChunk:
feat = feat.normalize()
# Concatenate label
datas = feat.concat([labelPdf, labelPho], axis=1)
# cut frames
if toDo == 'train':
if iterator.epoch >= 4:
datas = datas.cut(1000)
elif iterator.epoch >= 3:
datas = datas.cut(800)
elif iterator.epoch >= 2:
datas = datas.cut(400)
elif iterator.epoch >= 1:
datas = datas.cut(200)
elif iterator.epoch >= 0:
datas = datas.cut(100)
# Transform trainable numpy data
datas, _ = datas.merge(keepDim=True, sortFrame=True)
return datas
# Make data iterator
train = E.DataIterator(trainScpFile,
loadChunkData,
args.batchSize,
chunks=5,
shuffle=False,
otherArgs=(trainUttSpk, trainCmvnState,
trainLabelPdf, trainLabelPho, 'train'))
print('Generate train dataset. Chunks:{} / Batch size:{}'.format(
train.chunks, train.batchSize))
dev = E.DataIterator(devScpFile,
loadChunkData,
args.batchSize,
chunks=1,
shuffle=False,
otherArgs=(devUttSpk, devCmvnState, devLabelPdf,
devLabelPho, 'dev'))
print('Generate validation dataset. Chunks:{} / Batch size:{}.'.format(
dev.chunks, dev.batchSize))
print("Done.")
print('Prepare model...')
featDim = 40
if args.delta > 0:
featDim *= (args.delta + 1)
if args.splice > 0:
featDim *= (2 * args.splice + 1)
model = GRU(featDim, trainLabelPdf.target, trainLabelPho.target)
lossfunc = nn.NLLLoss()
if args.gpu >= 0:
model = model.cuda(args.gpu)
lossfunc = lossfunc.cuda(args.gpu)
print('Generate model done.')
print('Prepare optimizer and supporter...')
#lr = [(0,0.5),(8,0.25),(13,0.125),(15,0.07),(17,0.035),(20,0.02),(23,0.01)]
lr = [(0, 0.0004)]
print('Learning Rate:', lr)
optimizer = torch.optim.RMSprop(model.parameters(),
lr=lr[0][1],
alpha=0.95,
eps=1e-8,
weight_decay=0,
momentum=0,
centered=False)
lr.pop(0)
supporter = E.Supporter(args.outDir)
print('Done.')
print('Prepare test data...')
# Fmllr file
testFilePath = args.TIMITpath + '/data-fmllr-tri3/test/feats.scp'
testFeat = E.load(testFilePath)
# Use CMVN
if args.useCMVN:
testUttSpk = args.TIMITpath + '/data-fmllr-tri3/test/utt2spk'
testCmvnState = args.TIMITpath + '/data-fmllr-tri3/test/cmvn.ark'
testFeat = E.use_cmvn(testFeat, testCmvnState, testUttSpk)
# Add delta
if args.delta > 0:
testFeat = E.add_delta(testFeat, args.delta)
# Splice frames
if args.splice > 0:
testFeat = testFeat.splice(args.splice)
# Transform to array
testFeat = testFeat.array
# Normalize
if args.normalizeChunk:
testFeat = testFeat.normalize()
# Normalize acoustic model output
if args.normalizeAMP:
# compute pdf counts in order to normalize acoustic model posterior probability.
countFile = args.outDir + '/pdfs_counts.txt'
if not os.path.isfile(countFile):
_ = E.analyze_counts(aliFile=trainAliFile, outFile=countFile)
with open(countFile) as f:
line = f.readline().strip().strip("[]").strip()
counts = np.array(list(map(float, line.split())), dtype=np.float32)
normalizeBias = np.log(counts / np.sum(counts))
else:
normalizeBias = 0
print('Done.')
print('Prepare test data decode and score function...')
# Design a function to compute WER of test data
def wer_fun(model, feat, normalizeBias):
global args
# Tranform the formate of KaldiDict feature data in order to forward network
temp = E.KaldiDict()
utts = feat.utts
with torch.no_grad():
for index, utt in enumerate(utts):
data = torch.Tensor(feat[utt][:, np.newaxis, :])
data = torch.autograd.Variable(data)
if args.gpu >= 0:
data = data.cuda(args.gpu)
out1, out2 = model(data, is_training=False, device=args.gpu)
out = out1.cpu().detach().numpy() - normalizeBias
temp[utt] = out
print("(testing) Forward network {}/{}".format(
index, len(utts)),
end=" " * 20 + '\r')
# Tansform KaldiDict to KaldiArk format
print('(testing) Transform to ark', end=" " * 20 + '\r')
amp = temp.ark
# Decode and obtain lattice
hmm = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_test/final.mdl'
hclg = args.TIMITpath + '/exp/tri3/graph/HCLG.fst'
lexicon = args.TIMITpath + '/exp/tri3/graph/words.txt'
print('(testing) Generate Lattice', end=" " * 20 + '\r')
lattice = E.decode_lattice(amp, hmm, hclg, lexicon, args.minActive,
args.maxActive, args.maxMemory, args.beam,
args.latBeam, args.acwt)
# Change language weight from 1 to 10, get the 1best words.
print('(testing) Get 1-best words', end=" " * 20 + '\r')
outs = lattice.get_1best(lmwt=args.minLmwt,
maxLmwt=args.maxLmwt,
outFile=args.outDir + '/outRaw')
# If reference file is not existed, make it.
phonemap = args.TIMITpath + '/conf/phones.60-48-39.map'
outFilter = args.TIMITpath + '/local/timit_norm_trans.pl -i - -m {} -from 48 -to 39'.format(
phonemap)
if not os.path.isfile(args.outDir + '/test_filt.txt'):
refText = args.TIMITpath + '/data/test/text'
cmd = 'cat {} | {} > {}/test_filt.txt'.format(
refText, outFilter, args.outDir)
(_, _) = E.run_shell_cmd(cmd)
# Score WER and find the smallest one.
print('(testing) Score', end=" " * 20 + '\r')
minWER = None
for k in range(args.minLmwt, args.maxLmwt + 1, 1):
cmd = 'cat {} | {} > {}/test_prediction_filt.txt'.format(
outs[k], outFilter, args.outDir)
(_, _) = E.run_shell_cmd(cmd)
os.remove(outs[k])
score = E.wer('{}/test_filt.txt'.format(args.outDir),
"{}/test_prediction_filt.txt".format(args.outDir),
mode='all')
if minWER == None or score['WER'] < minWER:
minWER = score['WER']
os.remove("{}/test_prediction_filt.txt".format(args.outDir))
return minWER
print('Done.')
print('Now Start to Train')
for e in range(args.epoch):
print()
i = 0
usedTime = 0
supporter.send_report({'epoch': e})
# Train
model.train()
while True:
start = time.time()
# Get batch data and label
batch = train.next()
batch, lengths = E.pad_sequence(batch, shuffle=True, pad=0)
batch = torch.Tensor(batch)
data, label1, label2 = batch[:, :, 0:-2], batch[:, :,
-2], batch[:, :,
-1]
data = torch.autograd.Variable(data)
label1 = torch.autograd.Variable(label1).view(-1).long()
label2 = torch.autograd.Variable(label2).view(-1).long()
# Send to GPU if use
if args.gpu >= 0:
data = data.cuda(args.gpu)
label1 = label1.cuda(args.gpu)
label2 = label2.cuda(args.gpu)
# Clear grad
optimizer.zero_grad()
# Forward model
out1, out2 = model(data, is_training=True, device=args.gpu)
# Loss back propagation
loss1 = lossfunc(out1, label1)
loss2 = lossfunc(out2, label2)
loss = loss1 + loss2
loss.backward()
# Update parameter
optimizer.step()
# Compute accuracy
pred = torch.max(out1, dim=1)[1]
acc = 1 - torch.mean((pred != label1).float())
# Record train information
supporter.send_report({
'train_loss': float(loss),
'train_acc': float(acc)
})
ut = time.time() - start
usedTime += ut
batchLoss = float(loss.cpu().detach().numpy())
print(
"(training) Epoch:{}/{}% Chunk:{}/{}% Iter:{} Used-time:{}s Batch-loss:{} Speed:{}iters/s"
.format(e, int(100 * train.epochProgress), train.chunk,
int(100 * train.chunkProgress), i, int(usedTime),
"%.4f" % (batchLoss), "%.2f" % (1 / ut)),
end=" " * 5 + '\r')
i += 1
# If forwarded all data, break
if train.isNewEpoch:
break
# Evaluation
model.eval()
with torch.no_grad():
while True:
start = time.time()
# Get batch data and label
batch = dev.next()
batch, lengths = E.pad_sequence(batch, shuffle=True, pad=0)
maxLen, bSize, _ = batch.shape
batch = torch.Tensor(batch)
data, label1, label2 = batch[:, :,
0:-2], batch[:, :,
-2], batch[:, :, -1]
data = torch.autograd.Variable(data)
label1 = torch.autograd.Variable(label1).view(-1).long()
# Send to GPU if use
if args.gpu >= 0:
data = data.cuda(args.gpu)
label1 = label1.cuda(args.gpu)
# Forward model
out1, out2 = model(data, is_training=False, device=args.gpu)
# Compute accuracy of padded label
pred = torch.max(out1, dim=1)[1]
acc_pad = 1 - torch.mean((pred != label1).float())
# Compute accuracy of not padded label. This should be more correct.
label = label1.cpu().numpy().reshape([maxLen, bSize])
pred = pred.cpu().numpy().reshape([maxLen, bSize])
label = E.unpack_padded_sequence(label, lengths)
pred = E.unpack_padded_sequence(pred, lengths)
acc_nopad = E.accuracy(pred, label)
# Record evaluation information
supporter.send_report({
'dev_acc_pad': float(acc_pad),
'dev_acc_nopad': acc_nopad
})
ut = time.time() - start
usedTime += ut
batchLoss = float(loss.cpu().detach().numpy())
print(
"(Validating) Epoch:{}/{}% Chunk:{}/{}% Iter:{} Used-time:{}s Batch-loss:{} Speed:{}iters/s"
.format(e, int(100 * dev.epochProgress), dev.chunk,
int(100 * dev.chunkProgress), i, int(usedTime),
"%.4f" % (batchLoss), "%.2f" % (1 / ut)),
end=" " * 5 + '\r')
i += 1
# If forwarded all data, break
if dev.isNewEpoch:
break
print()
# We compute WER score from 4th epoch
if e >= 2:
minWER = wer_fun(model, testFeat, normalizeBias)
supporter.send_report({'test_wer': minWER})
# one epoch is over so collect information
supporter.collect_report(plot=True)
# Save model
def saveFunc(archs):
fileName, model = archs
torch.save(model.state_dict(), fileName)
supporter.save_arch(saveFunc, arch={'GRU': model})
# Change learning rate
if len(lr) > 0 and supporter.judge('epoch', '>=', lr[0][0]):
for param_group in optimizer.param_groups:
param_group['lr'] = lr[0][1]
lr.pop(0)
print("GRU Acoustic Model training done.")
print("The final model has been saved as:", supporter.finalArch["GRU"])
print('Over System Time:', datetime.datetime.now().strftime("%Y-%m-%d %X"))